Intelligent Optimization of Caching Operations in a Data Storage Device

ABSTRACT

Systems, methods and apparatus of intelligent optimization of caching operations in a data storage device. For example, the data storage device can include: one or more storage media components; a controller configured to store data into and retrieve data from the one or more storage media components; a cache memory configured to cache data that is stored in the one or more storage media components; and an artificial neural network configured to receive, as input and as a function of time, operating parameters indicative a data access pattern. The artificial neural network generates, based on the input, a prediction to determine an optimized set of cache configuration parameters; and cache operations of the cache memory are configured according to the optimized set of cache configuration parameters determined based on the prediction.

FIELD OF THE TECHNOLOGY

At least some embodiments disclosed herein relate to data storagedevices in general and more particularly, but not limited to,intelligent optimization of caching operations in a data storage deviceinstalled in a vehicle.

BACKGROUND

Recent developments in the technological area of autonomous drivingallow a computing system to operate, at least under some conditions,control elements of a motor vehicle without the assistance from a humanoperator of the vehicle.

For example, sensors (e.g., cameras and radars) can be installed on amotor vehicle to detect the conditions of the surroundings of thevehicle traveling on a roadway. A computing system installed on thevehicle analyzes the sensor inputs to identify the conditions andgenerate control signals or commands for the autonomous adjustments ofthe direction and/or speed of the vehicle, with or without any inputfrom a human operator of the vehicle.

In some arrangements, when a computing system recognizes a situationwhere the computing system may not be able to continue operating thevehicle in a safe manner, the computing system alerts the human operatorof the vehicle and requests the human operator to take over the controlof the vehicle and drive manually, instead of allowing the computingsystem to drive the vehicle autonomously.

Autonomous driving and/or advanced driver assistance system (ADAS)typically involves artificial neural network (ANN) for theidentification of events and/or objects that are captured in sensorinputs.

In general, an artificial neural network (ANN) uses a network of neuronsto process inputs to the network and to generate outputs from thenetwork.

For example, each neuron in the network receives a set of inputs. Someof the inputs to a neuron may be the outputs of certain neurons in thenetwork; and some of the inputs to a neuron may be the inputs providedto the neural network. The input/output relations among the neurons inthe network represent the neuron connectivity in the network.

For example, each neuron can have a bias, an activation function, and aset of synaptic weights for its inputs respectively. The activationfunction may be in the form of a step function, a linear function, alog-sigmoid function, etc. Different neurons in the network may havedifferent activation functions.

For example, each neuron can generate a weighted sum of its inputs andits bias and then produce an output that is the function of the weightedsum, computed using the activation function of the neuron.

The relations between the input(s) and the output(s) of an ANN ingeneral are defined by an ANN model that includes the data representingthe connectivity of the neurons in the network, as well as the bias,activation function, and synaptic weights of each neuron. Using a givenANN model a computing device computes the output(s) of the network froma given set of inputs to the network.

For example, the inputs to an ANN network may be generated based oncamera inputs; and the outputs from the ANN network may be theidentification of an item, such as an event or an object.

A spiking neural network (SNN) is a type of ANN that closely mimicsnatural neural networks. An SNN neuron produces a spike as output whenthe activation level of the neuron is sufficiently high. The activationlevel of an SNN neuron mimics the membrane potential of a naturalneuron. The outputs/spikes of the SNN neurons can change the activationlevels of other neurons that receive the outputs. The current activationlevel of an SNN neuron as a function of time is typically modeled usinga differential equation and considered the state of the SNN neuron.Incoming spikes from other neurons can push the activation level of theneuron higher to reach a threshold for spiking. Once the neuron spikes,its activation level is reset. Before the spiking, the activation levelof the SNN neuron can decay over time, as controlled by the differentialequation. The element of time in the behavior of SNN neurons makes anSNN suitable for processing spatiotemporal data. The connectivity of SNNis often sparse, which is advantageous in reducing computationalworkload.

In general, an ANN may be trained using a supervised method where theparameters in the ANN are adjusted to minimize or reduce the errorbetween known outputs resulted from respective inputs and computedoutputs generated from applying the inputs to the ANN. Examples ofsupervised learning/training methods include reinforcement learning, andlearning with error correction.

Alternatively, or in combination, an ANN may be trained using anunsupervised method where the exact outputs resulted from a given set ofinputs is not known before the completion of the training. The ANN canbe trained to classify an item into a plurality of categories, or datapoints into clusters.

Multiple training algorithms can be employed for a sophisticated machinelearning/training paradigm.

BRIEF DESCRIPTION OF THE DRAWINGS

The embodiments are illustrated by way of example and not limitation inthe figures of the accompanying drawings in which like referencesindicate similar elements.

FIG. 1 shows a system in which a vehicle is configured with a datastorage device to collect and process sensor data according to someembodiments.

FIG. 2 shows an autonomous vehicle having a data storage deviceaccording to one embodiment.

FIGS. 3-5 illustrate training of artificial neural networks formaintenance service prediction according to some embodiments.

FIG. 6 shows a method of predictive maintenance according to oneembodiment.

FIG. 7 shows a data storage device to accelerate neural networkcomputations according to one embodiment.

FIG. 8 shows a storage media component to accelerate neural networkcomputations according to one embodiment.

FIG. 9 shows a method to accelerate neural network computations in motorvehicles according to one embodiment.

FIG. 10 shows a data storage device configured to support neural networkcomputations according to one embodiment.

FIG. 11 illustrates the configuration of a namespace for an artificialneural network (ANN) model according to one embodiment.

FIG. 12 illustrates the configuration of a namespace for the inputs toartificial neurons according to one embodiment.

FIG. 13 illustrates the configuration of a namespace for the outputsfrom artificial neurons according to one embodiment.

FIGS. 14-16 show methods of predictive maintenance supported by a modelpartition, an input partition and an output partition according to oneembodiment.

FIG. 17 shows communications with a data storage device to implementneural network computation according to one embodiment.

FIG. 18 shows communications within a data storage device to implementneural network computation according to one embodiment.

FIG. 19 shows a method of communicating with a data storage device toimplement neural network computation according to one embodiment.

FIG. 20 shows a data storage device configured to optimize cacheoperations according to one embodiment.

FIG. 21 shows a method of optimizing cache operations in a data storagedevice according to one embodiment.

DETAILED DESCRIPTION

At least some embodiments disclosed herein provide systems, methods andapparatus to process sensor data generated in a motor vehicle, oranother vehicle with or without an advanced driver assistance system(ADAS).

Before a component of a motor vehicle breaks down or malfunctions duringthe operation of a vehicle, there can be indication of whether thecomponent needs replacement or maintenance. Such indications may not benoticeable to a typical driver or passengers. However, sensor data canbe collected and analyzed to predict the probability of componentfailures. The prediction can be used to schedule maintenance services,which can reduce or eliminate the chances of incidents where a componentof a vehicle breaks down or malfunctions during the operation of thevehicle on a roadway. Further, the prediction allows the service trip tobe scheduled at a convenient time.

For example, sensors can be installed in an automotive system to collectdata during its routine operations; and the sensor data can be used topredict whether and how soon a component needs replacement ormaintenance. The sensor data can be provided as input to an artificialneural network (ANN) (e.g., spiking neural network (SNN)) of anartificial intelligent (AI) system to train itself (e.g., using anunsupervised machine learn technique) in a time period in which thevehicle is expected to operate normally. The training customizes theneural network for the specific operating environment(s) of the driver,passenger, or user of the vehicle and the personalized operating habitsof the vehicle occupant(s). Subsequently, when the operating datadeviates the normal mode, the artificial neural network can detectabnormal conditions. The AI system can be used to suggest a maintenanceservice and/or identify the component that likely needs replacement ormaintenance.

FIG. 1 shows a system in which a vehicle is configured with a datastorage device to collect and process sensor data according to someembodiments.

The system of FIG. 1 includes a vehicle (111) having a data storagedevice (101). Optionally, the vehicle (111) has an advanced driverassistance system (ADAS) (105) and one or more sensors (103) thatprovide senor data input to the ADAS (105) and/or the data storagedevice (101). The data storage device (101) is configured to use anartificial neural network (ANN) (125) to predict/identify a need for amaintenance service based on the data collected by the sensors (103).The ADAS (105) can be omitted without impacting the predictivemaintenance features. In some implementations, at least a portion of thedata generate by the sensors (103) is used in both the ADAS (105) fordriver assistance and in the ANN (125) for maintenance prediction.Optionally, the output of the ANN (124) can be used in both the datastorage device (101) and in the ADAS (105).

The sensor(s) (103) can include digital cameras, lidars, radars,ultrasound sonars, brake sensors, speed sensors, acceleration sensors,airbag sensors, a GPS (global positioning system) receiver, audiosensors/microphones, vibration sensors, force/stress sensors,deformation sensors, motion sensors, temperature sensors, etc. Some ofthe sensors (103) can be configured primarily to monitor the environmentof the vehicle (111); and other sensors (103) can be configuredprimarily to monitor the operating condition of one or more component ofthe vehicle (111), such as an internal combustion engine, an exhaustsystem, an electric motor, a brake, a tire, a battery, etc.

The outputs of the sensor(s) (103) as a function of time are provided asa sensor data stream to the ADAS (105) and/or the ANN (125) to providedriver assistance (e.g., autonomous driving) and maintenance prediction.

For example, the vehicle (111) can have a wireless communication deviceto communicate with a remote server (119) via wireless signals (113) anda communication network (117). The remote server (119) is typicallyconfigured at a location away from a road (102) on which the vehicle(111) is in service. For example, the vehicle (111) may provide somesensor data (121) to the server (119) and receive update of the ANN(125) from the server (119).

One example of the communication network (117) is a cell phone networkhaving one or more base stations (e.g., 115) to receive the wirelesssignals (e.g., 113). Another example of the communication network (117)is internet, where the wireless local area network signals (e.g., 113)transmitted by the vehicle (113) is received in an access point (e.g.,115) for further communication to the server (119). In someimplementations, the vehicle (111) uses a communication link (107) to asatellite (109) or a communication balloon to communicate with theserver (119).

The server (119) can also communicate with one or more maintenanceservice facilities (e.g., 127) to receive maintenance service data (123)of vehicles (e.g., 111). The maintenance service data (123) can includeinspection records and/or service records of components of the vehicles(e.g., 111). For example, the inspection records and/or service recordscan indicate the degree of wear and tear of components inspected duringtheir services at the maintenance service facilities (e.g., 127), theidentification of failed or malfunctioning components, etc. The sensordata (121) of the vehicles (e.g., 111) in a time period prior to theservices and the maintenance service data (123) can be used to train anANN (125) to predict the probability of a component requiring amaintenance service. The updated ANN (125) can be used to predict andsuggest a maintenance service for a vehicle (111) based on sensor data(121) received in a recent period of time. Alternatively, the update ANN(125) can be transmitted to the vehicle (111); and the vehicle (111) canused the data generated from the sensors (103) during routine operationsof the vehicle (111) to predict and suggest a maintenance service.

The data storage device (101) of the vehicle (111) can be configured torecord sensor data for a period of time that can be used in the ANN forpredictive maintenance. Maintenance prediction is typically for arelative long period of time (e.g., a few days, weeks and/or months). Incontrast, sensor data recorded for the review of an accident, collision,or near collision involving an autonomous vehicle is typically for ashort period of time (e.g., 30 seconds to a few minutes). Thus, atypical black box data recorder configured to record sensor data for thereview/analysis of an accident or collision is insufficient forpredictive maintenance.

Optionally, the data storage device (101) stores the sensor data of aperiod of time leading to a trip to a maintenance service facility(e.g., 127). The maintenance service facility (e.g., 127) can downloadthe sensor data (121) from the data storage device (101) and provide thesensor data (121) and the corresponding maintenance service data (123)to the server (119) to facilitate the training of the ANN (125).

Optionally, or in combination, the data storage device (101) isconfigured with a machine learning module to customize and/or train theANN (125) installed in the vehicle (111) for predictive maintenance.

For example, the machine learning module of the data storage device(101) can be used to calibrate the ANN (125) to account for thetypical/daily environment in which the vehicle (111) is being operatedand/or driving preferences/habits of the driver(s) of the vehicle (111).

For example, during a period of time when the vehicle is expected to beoperated under typical/daily environment with healthy components, thesensor data generated by the sensors (103) can be used to train the ANN(125) to recognize the patterns of sensor data that represents troublefree operations. Such patterns can vary for different vehicles (e.g.,111) based on their routine operating environments and the drivinghabits/characteristics of their drivers. The training allows the ANN(125) to detect deviations from the recognized normal patterns andreport anomaly for maintenance predictions.

For example, the ANN (125) can include an SNN configured to classifytime-based variations of sensor data and/or detect deviation from knownpatterns of sensor data of the vehicle (111) operated in thenormal/healthy condition but in a personalized environment (e.g., adaily route of a driver/passenger) and/or operated under a personalizeddriving habit/pattern.

FIG. 2 shows an autonomous vehicle (111) having a data storage device(101) according to one embodiment. For example, the vehicle (111) in thesystem of FIG. 1 can be implemented using the autonomous vehicle (111)of FIG. 2.

The vehicle (111) of FIG. 2 is configured to have an advanced driverassistance system (ADAS) (105). The ADAS (105) of the vehicle (111) canhave an Artificial Neural Network (ANN) (125) for object detection,recognition, identification, and/or classification. The ANN (125) and/oranother neural network (e.g., configured in the data storage device(101)) can be used to predict the probability of a component of thevehicle (111) requiring a maintenance service (e.g., repair,replacement, or adjustment).

Preferably, the data storage device (101) is configured to processsensor data at least partially for predictive maintenance with reducedcomputation burden on the processors (133) that are tasked to operatethe ADAS (105) and/or other components, such as an infotainment system(149).

The vehicle (111) typically includes an infotainment system (149), acommunication device (139), one or more sensors (103), and a computersystem (131) that is connected to some controls of the vehicle (111),such as a steering control (141) for the direction of the vehicle (111),a braking control (143) for stopping of the vehicle (111), anacceleration control (145) for the speed of the vehicle (111), etc. Insome embodiments, the vehicle (111) in the system of FIG. 1 has asimilar configuration and/or similar components.

Some of the sensors (103) are required for the operations of the ADAS(105); and some of the sensors (103) are used to collect data related tothe health of the components of the vehicle (111), which may not be usedin the ADAS (105). Optionally, the sensor data generated by the sensors(103) can also be used to predict the likelihood of imminent failure ofa component. Such a prediction can be used in the ADAS (105) to takeemergency actions to render the vehicle in a safe state (e.g., byreducing speed and/or pulling off to park).

The computer system (131) of the vehicle (111) includes one or moreprocessors (133), a data storage device (101), and memory (135) storingfirmware (or software) (147), including the computer instructions anddata models for ADAS (105).

The one or more sensors (103) of the vehicle can include a visible lightcamera, an infrared camera, a lidar, radar, or sonar system, aperipheral sensor, a global positioning system (GPS) receiver, asatellite positioning system receiver, a brake sensor, and/or an airbagsensor. Further, the sensors (103) can include audio sensors (e.g.,microphone) configured to monitor noises from various components andlocations in the vehicle (111), a vibration sensor, a pressure sensor, aforce sensor, a stress sensor, and/or a deformation sensor configured tomeasure loads on a component of the vehicle (111), accelerometers and/orgyroscope sensors measuring the motions of some components of thevehicle (111), etc. Such sensors can be used to monitor the operatingstatus and/or health of the components for predictive maintenance.

The sensor(s) (103) can provide a stream of real time sensor data to thecomputer system (131). The sensor data generated by a sensor (103) ofthe vehicle (111) can include an image that captures an object using acamera that images using lights visible to human eyes, or a camera thatimages using infrared lights, or a sonar, radar, or LIDAR system. Imagedata obtained from at least one sensor of the vehicle is part of thecollected sensor data for recording in the data storage device (101)and/or as input to the ANN (125). For example, a camera can be used toobtain roadway information for the travel of the vehicle (111), whichcan be processed by the ANN (125) to generate control signals for thevehicle (111). For example, a camera can be used to monitor theoperation state/health of a component of the vehicle (111), which can beprocessed by the ANN (125) to predict or schedule a maintenance service.

The sensor data generated by a sensor (103) of the vehicle (111) caninclude an audio stream that captures the characteristics of sounds at alocation on the vehicle (111), such as a location near an engine, amotor, a transmission system, a wheel, a door, a window, etc. The audiodata obtained from at least one sensor (103) of the vehicle (111) can bepart of the collected sensor data for recording in the data storagedevice (101) and/or as input to the ANN (125). For example, the audiostream can be used to monitor the operation state/health of a componentof the vehicle (111) (e.g., an internal combustion engine, an exhaustsystem, an electric motor, a brake), which can be processed by the ANN(125) to predict or schedule a maintenance service.

The infotainment system (149) can be used to present the predicted orschedule maintenance service. Optionally, the communication device (139)can establish a connection to a mobile device of the driver of thevehicle (111) to inform the driver of the recommended maintenanceservice and/or the recommended data of the service, to calendar theappointment, etc.

When the vehicle (111) is configured with an ADAS (105), the outputs ofthe ADAS (105) can be used to control (e.g., (141), (143), (145)) theacceleration of the vehicle (111), the speed of the vehicle (111),and/or the direction of the vehicle (111), during autonomous driving.

FIGS. 3-5 illustrate training of artificial neural networks formaintenance service prediction according to some embodiments.

In FIG. 3, a module (171) of supervised machine learning is used totrain an artificial neural network (125) to minimize the differencesbetween the service prediction (129) generated from the sensor data(121) and the maintenance service data (123).

For example, the maintenance service data (123) can identify themeasured wear and tear of a component as a function of time to predict atime to a recommended service. The sensor data (121) can be used in theANN (125) to generate a predicted time to the recommended service. Thesupervised machine learning module (171) can adjust the artificialneural network (125) to reduce/minimize the difference between the timepredicted based on the sensor data (121) and the time computed from themeasurement of wear and tear.

For example, the maintenance service data (123) can identify a componentthat is replaced or repaired in the maintenance service facility (127).The sensor data (121) recorded for a time period prior to thereplacement or repair of the component can be used to calculate thetimes to the replacement or repair. Further, the segments of the streamof sensor data in the time period before the replacement or repair canbe used in the ANN (125) to generate a prediction to the time of thereplacement or repair. The supervised learning (171) can be used toadjust the ANN (125) to reduce the predicted time to the replacement orrepair and the actual time to the replacement or repair.

The supervised learning (171) of FIG. 2 can be applied in the server(119) based on the sensor data of a population of vehicles and theirmaintenance service data (123) to generate a generic ANN for thepopulation of the vehicles.

The supervised learning (171) of FIG. 2 can be applied in the vehicle(111) based on the sensor data of the vehicle and its maintenanceservice data (123) to generate a customized/personalized ANN for thepopulation of the vehicles. For example, a generic ANN can be initiallyused in the vehicle (111); and the sensor data of the vehicle (111) andits maintenance service data (123) can be used to further train the ANN(125) of the vehicle for customization/personalization of the ANN (125)in the vehicle (111).

In FIG. 4, a module (175) of unsupervised machine learning is used totrain or refine an artificial neural network (125) to facilitate anomalydetection (173). The unsupervised machine learning module (175) isconfigured to adjust the ANN (e.g., SNN) the classification, clustering,or recognized pattern in the sensor data (121) such that a degree ofdeviation from the classification, clustering, or recognized pattern inthe sensor data (121) generated in a recent time period can be used tosignal the detection (173) of anomaly. Anomaly detection (173) allowsthe vehicle (111) to be scheduled for inspection in a maintenanceservice facility (127). Optionally, after the inspection, maintenanceservice data (123) can be used to apply a supervised learning (171) togenerate more precise predictions to a service, as in FIG. 3.

Typically, a vehicle (111) can be assumed to be operating in anormal/healthy condition in a certain time period. For example, after anew vehicle (111) is initially delivered for service, the vehicle (111)can be assumed to provide trouble-free services for at least a period oftime (e.g., a few months). For example, after a time period followingthe replacement or repair of a component, the component can be assumedto provide trouble-free service for at least a period of time (e.g., afew months or a year). Thus, the sensor data (121) obtained during thisperiod of time can be pre-classified as “normal” to train the ANN (125)using an unsupervised learning (175) as in FIG. 4, or a supervisedlearning (171) as in FIG. 5.

For example, the sensor data (121) collected via during the “normal”service time period of the vehicle (111) or a component can beclassified via an unsupervised learning (175) into a number of clusters.Different clusters may correspond to different types of normalconditions (e.g., traveling on different routes, on roads with differentsurface conditions, on days with different whether conditions, indifferent time periods of a day, different days in a week, differentmood of driving habits of the driver). When a subsequent sensor data(121) is classified outside of the “normal” clusters, an anomaly isdetected.

Optionally, a supervised machine learning (171) can be used to train theANN (125), as illustrated in FIG. 5. During the “normal” service periodof the vehicle (111) or a component, an expected classification (177)can be used to label the sensor data (121). The supervised learning(171) can be used to minimize the classification differences between thepredictions (179) made using the ANN (125) according to the sensor data(121) and the expected classification (177). Further, when the sensordata (121) is known to be “abnormal” (e.g., after a diagnosis made inthe maintenance service facility (127) or by the user, driver, orpassenger of the vehicle (111)), the expected classification (177) canbe changed to “abnormal” for further training of the ANN (125) fordirect recognition of the anomaly (e.g., instead of relying upondeviation from known “normal” clusters for an inference of anomaly).

Thus, the ANN (125) can be trained to identify abnormal sensor data andestimate the degree of severity in anomaly to schedule a maintenanceservice.

FIG. 6 shows a method of predictive maintenance according to oneembodiment. For example, the method of FIG. 6 can be implemented in thedata storage device (101) of FIG. 1 or 2 in the vehicle (111) or in acomputer system (131) in the vehicle (111) of FIG. 2.

At block 201, a sensor (e.g., 103) installed in a vehicle (111)generates a sensor data stream (e.g., 121) during operations of thevehicle (111) on a road (102).

At block 203, the sensor data stream (e.g., 121) is provided into anartificial neural network (ANN) (125). For example, the ANN (125) caninclude a spiking neural network (SNN).

At block 205, the artificial neural network (ANN) (125) generates, basedon the sensor data stream (e.g., 121), a prediction of a maintenanceservice.

At block 207, a data storage device (101) configured on the vehiclestores at least a portion of the sensor data stream (e.g., 121).

At block 209, the artificial neural network (ANN) is trained using thesensor data stream (e.g., 121) collected within a predetermined timeperiod from the vehicle leaving a factory or a maintenance servicefacility (127).

For example, the artificial neural network (ANN) can be configured toidentify a component of the vehicle (111) that needs repair orreplacement in the maintenance service and/or identify a predicted timeperiod to failure or malfunctioning of the component, or a suggestedtime period to a recommended maintenance service of the component priorthe failure or malfunctioning of the component. Thus, the performance ofthe predicted maintenance service can avoid an incident of failure ormalfunctioning of the component while the vehicle (111) operates on theroad (102).

For example, the sensor (103) can be a microphone mounted in vicinity ofthe component, a vibration sensor attached to the component, a pressuresensor installed in the component, a force or stress sensor mounted onor attached to the component, a deformation sensor attached to thecomponent, an accelerometer configured to measure motion parameters ofthe component.

Optionally, the data storage device (101), the computer system (131) ofthe vehicle (111), and/or a server (119) remote from the vehicle canhave a machine learning module configured to train the artificial neuralnetwork (ANN) (125) during a period of time in which the vehicle (111)is assumed to be in a healthy state, such as a predetermined time periodfrom the vehicle (111) leaving a factory or a maintenance servicefacility (127).

For example, the machine learning module can use an unsupervised machinelearning (175) to train the ANN (125) to recognize/classify normalpatterns of sensor data (121) and thus to have the capability to detectanomaly based on deviation from the normal patterns, as illustrated inFIG. 4. Alternatively, supervised machine learning (171) can be used, asillustrated in FIG. 3 or 5.

For example, unsupervised machine learning (175) can be applied by thedata storage device (101) or the computer system (131) of the vehicle(111) during the predetermined period of time in which the vehicleand/or the component is known to be operating without troubles ordegradations.

Alternatively, or in combination, some of the sensor data (121) storedin the data storage device (101) of the vehicle (111) can be uploaded tothe server (119) for training the ANN (125).

In at least some embodiments disclosed herein, the data storage device(101) is configured to accelerate the computations of the artificialneural network (ANN) (125) of the vehicle (111).

For example, in addition to the typical operations to support dataaccess and storage, the data storage device (101) can be furtherconfigured to perform at least part of the computations involving theartificial neural network (ANN) (125), such as the generation of thepredictions (e.g., 129 or 173) or classifications (e.g., 179) from thesensor data (121) and/or the adjustment of the ANN (125) throughunsupervised machine learning (175) (e.g., as illustrated in FIG. 4)and/or supervised machine learning (171) (e.g., as illustrated in FIG. 3or 5).

For example, the computations configured in the data storage device(101) can be used to reduce the amount of data to be transmitted to theprocessor(s) (133) to use or apply the ANN (125) and/or reduce thecomputation tasks of the processor(s) (133) in evaluating the outputs ofthe ANN (125) and/or in training the ANN (125). Such an arrangement canresult in faster output from the data storage device (101) and/or lowerenergy usage, since the data would not have to be moved in and out ofthe memory to a dedicated, standalone neural network accelerator. Thecomputation capability of the data storage device (101) in processingdata related to the ANN (125) enables the computer system (131) of themotor vehicle (111) to monitor the health of the automotive components(e.g., in a non-real-time manner, or a pseudo-real-time manner), withreduced impact, or no impact, on the processing of mission criticaltasks (e.g., autonomous driving by the ADAS (105)). Further, thecomputation capability of the data storage device (101) can be used toaccelerate the processing of the sensor data for ADAS (105) and thusimprove the processing of mission critical tasks.

FIG. 7 shows a data storage device (101) to accelerate neural networkcomputations according to one embodiment. For example, the data storagedevice (101) of FIG. 7 can be used to implement the data storage device(101) of the vehicle (111) illustrated in FIG. 1 or 2.

In FIG. 7, the data storage device (101) has a host interface (157)configured to communicate with a processor (e.g., 133). For example, thecommunication between the processor (e.g., 133) and the host interface(157) can be, at least in part, in accordance with a communicationprotocol for a peripheral component interconnect express (PCIe) bus, aserial advanced technology attachment (SATA) bus, a universal serial bus(USB) bus, and/or a storage area network (SAN).

For example, the host interface (157) can be used to receive sensor data(121) generated by the sensors (103) of the vehicle (111) to optionallystore a portion of the sensor data (121) in the storage media components(161 to 163).

For example, each of the storage media components (161 to 163) can be amemory integrated circuit configured to store data. For example, a mediacomponent (161 or 163) can include one or more integrated circuit diesembedded in an integrated circuit package. An integrated circuit die canhave many memory units formed thereon to store data.

In general, some memory integrated circuits are volatile and requirepower to maintain the stored data; and some memory integrated circuitsare non-volatile and can retain the stored data even when not powered.

Examples of non-volatile memory include flash memory, memory unitsformed based on negative-and (NAND) logic gates, negative-or (NOR) logicgates, phase-change memory (PCM), magnetic memory (MRAM), resistiverandom-access memory, cross point storage and memory devices. A crosspoint memory device uses transistor-less memory elements, each of whichhas a memory cell and a selector that are stacked together as a column.Memory element columns are connected via two perpendicular lays ofwires, where one lay is above the memory element columns and the otherlay below the memory element columns. Each memory element can beindividually selected at a cross point of one wire on each of the twolayers. Cross point memory devices are fast and non-volatile and can beused as a unified memory pool for processing and storage. Furtherexamples of non-volatile memory include Read-Only Memory (ROM),Programmable Read-Only Memory (PROM), Erasable Programmable Read-OnlyMemory (EPROM) and Electronically Erasable Programmable Read-Only Memory(EEPROM) memory, etc. Examples of volatile memory include DynamicRandom-Access Memory (DRAM) and Static Random-Access Memory (SRAM).

The data storage device (101) can have a controller (151) that includesvolatile local memory (153) and at least one processing device (155).

The local memory of the controller (151) can be an embedded memoryconfigured to store instructions for performing various processes,operations, logic flows, and routines that control operation of theprocessing device (155), including handling communications between thedata storage device (101) and the processor(s) (e.g., 133) of thevehicle (111), and other functions described herein. The local memory(151) of the controller (151) can include read-only memory (ROM) forstoring micro-code and/or memory registers storing, e.g., memorypointers, fetched data, etc., and/or volatile memory, such as DynamicRandom-Access Memory (DRAM) and Static Random-Access Memory (SRAM).

In FIG. 7, the data storage device (101) includes a neural networkaccelerator (159) coupled to the controller (151) and/or the storagemedia components (161 to 163).

For example, the neural network accelerator (159) can be configured toperform matrix arithmetic computations. The computations involving ANN(125) have matrix multiplication and accumulation operations, which canbe computational intensive for a generic processor (e.g., 133). Usingthe neural network accelerator (159) to perform the matrix arithmeticcomputations can reduce the data to be transmitted to the processor(s)(133) of the vehicle (111) and reduce the computation workload for theprocessor(s) (133).

For example, when the ANN (125) includes a spiking neural network (SNN),the simulation of the differential equation(s) for controlling theactivation level of SNN neurons can be computationally intensive for ageneric processor (e.g., 133). The neural network accelerator (159) canuse special hardware to simulate the differential equation(s) and thusimprove the computational efficiency of the computer system (131) as awhole.

In some implementations, the neural network accelerator (159) is anintegrated circuit device separate from the controller (151) and/or thestorage media components (161 to 163). Alternatively, or in combination,a neural network accelerator (159) is integrated with the controller(151) in an integrated circuit package. Further, a neural networkaccelerator (159) can be integrated in at least one of the storage mediacomponents (161 to 163), as illustrated in FIG. 8.

FIG. 8 shows a storage media component (160) to accelerate neuralnetwork computations according to one embodiment. For example, each orsome of the storage media components (161 to 163) in FIG. 7 can beimplemented using a storage media component (160) of FIG. 8.

In FIG. 8, the storage media component (160) can be housed within anintegrated circuit package. An input/output (I/O) interface (171) of thestorage media component (160) is configured to process input/outputsignals in the pins of the integrated circuit package. For example, theinput/output signals can include address signals to specify locations inthe media units (175) and data signals representing data to be writtenin the media units (175) at the locations specified via the addresssignals, or data retrieved from the locations in the media units (175).

In FIG. 8, a neural network accelerator (159) is coupled with thecontrol logic (173) and/or the media units (175) to perform computationsthat are used in the evaluation of the output of an ANN (125) and/or inthe training of the ANN (125).

For example, the input/output interface (171) can receive addresses thatidentify matrices that are stored in the media units and that are to beoperated upon via the neural network accelerator (159). The storagemedia component (160) can provide the computation results of the neuralnetwork accelerator (159) as the output data responsive to theaddresses, store the output data in a buffer for further operations,store the output data into a location in the media units (175) specifiedvia the address signals. Thus, the computations performed by the neuralnetwork accelerator (159) can be within the storage media component(160), which is close to the media units (175) in which the matrix datais stored. For example, each of the media units (175) can be anintegrated circuit die on which memory units of non-volatile memory areformed.

For example, the state data of SNN neurons can be stored in the mediaunits (175) according to a predetermined pattern. The neural networkaccelerator (159) can automatically update the states of the SNN neuronsaccording to the differential equation(s) for controlling the activationlevel of SNN neurons over time. Optionally, the neural networkaccelerator (159) is configured to processing spiking of neurons in theneural network. Alternatively, the neural network accelerator (159) ofthe data storage device (101) and/or the processor(s) (133) can beconfigured to process the spiking of neurons and/or accumulation ofinputs to the SNN.

FIG. 9 shows a method to accelerate neural network computations in motorvehicles according to one embodiment. For example, the method of FIG. 9can be implemented in a vehicle (111) of FIG. 1 or 2 using a datastorage device (101) of FIG. 7 and/or a storage media component (160) ofFIG. 8. For example, the method of FIG. 9 can be used in combinationwith the method of FIG. 6.

At block 221, a data storage device (101) of a vehicle (111) receives asensor data stream from at least one sensor (e.g., 103) configured onthe vehicle (111).

At block 223, the data storage device (101) stores at least a portion ofthe sensor data stream.

At block 225, a neural network accelerator (159) configured within thedata storage device (101) performs at least a portion of computationsbased on an artificial neural network (125) and the sensor data stream.

At block 227, a maintenance service of the vehicle (111) is predictedbased at least in part on the computations performed by the neuralnetwork accelerator (159) configured within the data storage device(101).

Optionally, at block 229, the artificial neural network (ANN) is trainedin the vehicle (111), using at least in part the neural networkaccelerator and using the sensor data stream collected within apredetermined time period, such as a period following the delivery ofthe new vehicle (111) from a factory or following the replacement of acomponent in a maintenance service facility (127).

For example, the neural network accelerator (159) can be configured onan integrated circuit device that is separate from a controller (151) ofthe data storage device and/or separate from the storage mediacomponents (161 to 163).

For example, the neural network accelerator (159) can be configured onan integrated circuit device that includes a controller (151) of thedata storage device (101), or on an integrated circuit device thatincludes storage media component (160, 1061 or 163) of the data storagedevice (101).

For example, the neural network accelerator (159) can be configured toperform computations, such as matrix arithmetic computations for ANNand/or or differential equation simulations for SNN, using data storedin the data storage device (101).

Examples of the matrix arithmetic computations include matrixmultiplication and accumulation operations. After a computation togenerate a result of the matrix arithmetic computations using a datastored in the data storage device (101), the neural network accelerator(159) can provide the result as output of the data storage device (111)in retrieving data (e.g., in response to a read command). Alternatively,or in combination, the result of the matrix arithmetic computation canbe buffered in the data storage device (101) as operand for a nextmatrix computation performed in combination with a matrix of dataretrieved from the non-volatile memory via a read command received inthe host interface (157).

When the artificial neural network (ANN) (125) includes a spiking neuralnetwork (SNN), the neural network accelerator can be configured tosimulate a differential equation controlling activation levels ofneurons in the spiking neural network (SNN). Optionally, the storagemedia component is configured to store states of the neurons in thespiking neural network according to a predetermined pattern; and theneural network accelerator is configured to automatically update thestates of the neurons over time according to the differential equation.For example, the neural network accelerator (159) can be configured totrain the spiking neural network (SNN) via unsupervised machine learningto detect anomaly.

The computations performed by the neural network accelerator (159)according to an artificial neural network (ANN) (125) involve differenttypes of data that have different patterns of usages of the data storagedevice (101).

For example, making a prediction using the artificial neural network(ANN) (125) includes the use of data specifying the model of theartificial neural network (ANN) (125), input data provided to theartificial neurons, and output data generated by the artificial neurons.

The storage capacity of the data storage device (101) can be partitionedinto different portions for the different types of ANN-related data. Thedifferent portions can be separately configured to optimize the accessand storage of the corresponding data according to their patterns ofusages by the neural network accelerator (159) and/or the processor(s)(133) of the computer system (131) in which the data storage device(101) is configured.

The model of the artificial neural network (ANN) (125) can include theparameters specifying the static attributes of individual artificialneurons in the ANN (125) and the neuron connectivity in the ANN (125).The model data of the ANN (125) is static and does not change during theprediction calculation made using the ANN (125). Thus, the usage patternof the model data is mostly read. However, the model data of the ANN(125) can change when an updated ANN (125) is installed. For example,the vehicle (111) can download an updated ANN (125) from the server(119) to the data storage device (101) of the vehicle (111) to updateits prediction capability. The model data of the ANN (125) can alsochange during or after the training of the ANN (125) using a machinelearning technique (e.g., 171 or 175). It is preferred to configure aseparate partition or namespace of the data storage device (101) tostore the model data, where the partition or namespace is operatedaccording to configuration parameters that optimize the memory units forthe specific usage patterns of the model data (e.g., mostly read,infrequent update). For example, when the memory units are implementedusing a flash memory based on NAND logic gates, the memory units in theANN model partition/namespace can be configured to operate in amulti-level cell (MLC) mode, a triple level cell (TLC) mode, or aquad-level cell (QLC) mode, wherein each memory cells stores two, three,or four bits for increased storage capability.

Input data provided to the artificial neurons in the ANN (125) caninclude external inputs and internal inputs. The external inputs aregenerated typically by the sensors (103) of the vehicle (111) but not byartificial neurons in the ANN (125). The external inputs can be saved ina cyclic fashion so that the input data of the most recent time periodof a predetermined length of driving can be found in the data storagedevice (101). Thus, it is preferred to configure a separate partition ornamespace of the data storage device (101) to store the external inputdata, where the partition or namespace is operated according toconfiguration parameters that optimize the memory units for the storagepattern of the external input data (e.g., enhanced endurance, cyclicoverwrite). For example, when the memory units are implemented using aflash memory based on NAND logic gates, the memory units in the ANNinput partition/namespace can be configured to operate in a single levelcell (SLC) mode, where each memory cell stores one bit of data forimproved endurance in cyclic overwriting operations.

In some implementations, artificial neurons can have state variablesthat change over time in response to inputs during predictioncalculations. For example, the activation level of a spiking neuron canchange over time and is considered a dynamic state variable of thespiking neuron. In some implementations, such state variable data ofartificial neurons has a similar storage usage pattern as the externalinput data; and thus, the state variable data can be stored in thepartition or namespace configured for the external input data. In otherimplementations, the state variable data of artificial neurons is keptin a buffer and stored less frequently than the external inputs; andthus, another partition/namespace can be configured for storing thedynamic state variable data of artificial neurons.

Output data generated by the artificial neurons in the ANN (125) can bebuffered for further access by the neural network accelerator (159)and/or the processor(s) (133) of the computer system (131). The outputdata can include external outputs and internal outputs. The externalinputs are generated by artificial neurons as the output from the ANN(125), such as the results of classifications or predictions made by theANN (125). The output of the ANN (125) is typically further processed bythe processor(s) (133) of the computer system (131). The external inputsmay be saved periodically (e.g., in a way similar to the storing of thestate variable data). The internal outputs and/or some of the externaloutputs can be internal inputs to artificial neurons in the ANN (125).In general, it may not be necessary to store the internal outputs fromthe buffer of the data storage device to the storage media components.In some implementations, when the buffer capability of the data storagedevice (101) is insufficient to hold the entire state variable dataand/or the internal outputs, the data storage device (101) can use aswap partition/namespace to extend the capacity of the buffer. The swappartition/namespace can be configured for optimized random access andfor improved endurance.

External outputs and/or dynamic states of neurons can be saved in aseparate output partition or namespace, in a cyclic way so that theexternal output data and/or dynamic states of the neurons can beperiodically stored, and the most recent sets of the external outputsand/or dynamic states can be found in the data storage device (101).External outputs and/or dynamic states of neurons can be storedselectively, since some of such data can be re-generated by the ANN fromthe external inputs stored in the input partition or namespace.Preferably, the output partition or namespace is configured to store oneor more sets of external outputs and/or dynamic states that cannot becreated from the external inputs stored in the input partition ornamespace. In storing data in a cyclic way in an input/output partitionor namespace, the oldest stored data sets are erased to make rooms forthe most recent data sets. The ANN input/output partition/namespace canbe configured for an optimized sequential write stream for copying datafrom the buffer of the data storage device into the memory units in thestorage media components of the data storage device.

FIG. 10 shows a data storage device (101) configured to support neuralnetwork computations according to one embodiment. For example, the datastorage device (101) can be used in the vehicle (111) in FIG. 1 or 2 tofacilitate predictive maintenance and/or support the ADAS (105).

The data storage device (101) of FIG. 10 includes a host interface (157)and a controller (151), similar to the data storage device (101) of FIG.7.

The storage capacity (181) of the data storage device (101) of FIG. 10can be implemented using a set of storage media components, similar tothe storage media components (161 to 163) in the data storage device(101) of FIG. 7.

A set of namespaces (183, 185, 187, . . . ) can be created on thestorage capacity (181) of the data storage device (101). Each of thenamespace (e.g., 183, 185, or 187) corresponds to a named portion of thestorage capacity (181). Logical addresses are defined within eachnamespace. An address map (191) is configured to map between the logicaladdresses defined in the namespaces (183, 185, 187, . . . ) to thephysical addresses of memory units in the storage media components(e.g., 161 to 163 illustrated in FIG. 7).

The address map (191) can include namespace optimization settings (192)for the namespaces (183, 185, and 187).

For example, an ANN model namespace (183) can be a memory/storagepartition configured for the model data of the artificial neural network(ANN) (125). The namespace optimization settings (192) optimizes thememory operations in the ANN model namespace (183) according to the datausage pattern of ANN models (e.g., mostly read, infrequent updatecentric).

For example, a neuron input namespace (185) can be a memory/storagepartition configured for the external input data to the artificialneural network (ANN) (125). The namespace optimization settings (192)optimizes the memory operations in the neuron input namespace (185)according to the data usage pattern of the external input data (e.g.,for enhanced endurance supporting cyclic overwrite of continuous inputdata flow for sequential writes).

For example, a neuron output namespace (187) can be a memory/storagepartition/configured for the external output data provided from theartificial neural network (ANN) (125). The namespace optimizationsettings (192) optimizes the memory operations in the neuron outputnamespace (187) according to the data usage pattern of the externaloutput data (e.g., improved endurance for periodically overwrite of datawith random read/write access).

The data storage device (101) includes a buffer (152) configured tostore temporary/intermediate data of the artificial neural network (ANN)(125), such as the internal inputs/outputs of the artificial neurons inthe ANN (125).

Optionally, a swap namespace can be configured in the storage capacity(181) to extend the capacity of the buffer (152).

Optionally, the address map (191) includes a mapping between logicmemory addresses received in the host interface (157) to access data ofartificial neurons and the identities of the artificial neurons. Thus, aread or write command to access one type of data of an artificial neuronin one namespace can cause the controller 151 to access another type ofdata of the artificial neuron in another namespace.

For example, in response to a request to write external input data for aneuron into the storage capacity (181) of the data storage device (185),the address map (191) can be used to calculate the addresses of themodel parameters of the neuron in the ANN model namespace (183) and readthe model parameters into the buffer (152) to allow the neural networkaccelerator (159) to perform the computation of the output of theneuron. The output of the neuron can be saved in the buffer (152) as theinternal input to other neurons (e.g., to reduce write amplification).Further, the identities of the other neurons connected to the neuron canalso be retrieved from the ANN model namespace (183) into the buffer(152), which allows the neural network accelerator (159) and/or theprocessor to further process the propagation of the output in the ANN(125). The retrieval of the model data from the ANN model namespace(183) can be performed in parallel with the storing of the externalinput data into the neuron input namespace (185). Thus, the processors(133) of the computer system (131) of the vehicle (111) do not have toexplicitly send in read commands for the retrieval of the model datafrom the ANN model namespace (183).

Similarly, in response to reading output data of a neuron, the addressmap (191) can be used to compute the addresses of the model parametersof the neuron stored in the ANN model namespace (183) and read the modelparameters into the buffer (152) to allow the neural network accelerator(159) to apply internal inputs in the buffer (152) to the perform thecomputation of the output of the neuron. The computed output can beprovided as a response to the reading of the output data for the neuron,without the data storage device (101) having to store the output data inthe storage media components (e.g., 161 to 163). Thus, the processors(133) and/or the neural network accelerator (159) can control thecomputations of the neuron via writing inputs to neurons and/or readingoutputs from neurons.

In general, incoming external input data to the ANN (125) can be rawsensor data (121) generated directly by the sensors (103) withoutprocessing by the processors (133) and/or the neural network accelerator(159). Alternatively, indirect sensor data (121) that has processed bythe processors (133) for the ANN (125) from the signals from the sensors(103) can be provided as the external input data. The incoming externalinput data can be accepted in the host interface (157) and written in acyclic way into the neuron input namespace (185), and automaticallybuffered in the buffer (152) for neural network accelerator (159) togenerate neuron outputs using the model stored in the ANN modelnamespace (183). The outputs generated by the neural network accelerator(159) can be further buffered as internal inputs for further applicationof the model in the ANN model namespace (183). When the external outputsbecome available, the data storage device (101) can report thecompletion of the write requests with an indication of the availabilityof the external outputs. Optionally, the controller 151 and/or theneural network accelerator (159) can generate internal read commands topropagate signals in the ANN (125) in generating the external outputs.Alternatively, the host processors (133) can control the propagation ofsignals in the ANN (125) by selectively reading outputs of neurons; andthe data storage device (101) can actively buffer data that may beneeded in the buffer (152) to accelerate the ANN computation.

FIG. 11 illustrates the configuration of a namespace (183) for anartificial neural network (ANN) model according to one embodiment. Forexample, the configuration of FIG. 11 can be implemented in the datastorage device (101) illustrated in FIGS. 7 and/or 10. For example, thesettings (193) of FIG. 11 can be part of the namespace optimizationsettings (192) of FIG. 10.

The configuration of FIG. 11 maps an ANN model namespace (183) to atleast one storage media component A (161). Preferably, the at least onestorage media component A (161) can be used by the controller (151) inparallel with storage media components (e.g., 163) that hosts the othernamespaces (e.g., 185 and 187) of ANN data. For example, the storagemedia component A (161) can be in an integrated circuit package that isseparate from the integrated circuit packages for the other namespaces(e.g., 185 and 187). Alternatively, the storage media components (161 to163) are formed on separate integrated circuit dies embedded in a sameintegrated circuit package. Alternatively, the storage media components(161 to 163) can be formed on separate regions of an integrated circuitdie, where the separate regions can be operated substantially inparallel (e.g., for read, for erase, and/or for write).

In FIG. 11, the settings (197) are optimized to the usage pattern ofmostly read and infrequent update.

FIG. 12 illustrates the configuration of a namespace (185) for theinputs to artificial neurons according to one embodiment. For example,the configuration of FIG. 11 can be implemented in the data storagedevice (101) illustrated in FIGS. 7 and/or 10. For example, the settings(195) of FIG. 11 can be part of the namespace optimization settings(192) of FIG. 10.

The configuration of FIG. 12 maps a neuron input namespace (185) to atleast one storage media component B (163). Preferably, the at least onestorage media component B (163) can be used by the controller (151) inparallel with storage media components (e.g., 161) that hosts the othernamespaces (e.g., 183 and 187) of ANN data. For example, the storagemedia component B (163) can be in an integrated circuit package that isseparate from the integrated circuit packages for the other namespaces(e.g., 183 and 187). Alternatively, the storage media components (161 to163) are formed on separate integrated circuit dies embedded in a sameintegrated circuit package. Alternatively, the storage media components(161 to 163) can be formed on separate regions of an integrated circuitdie, where the separate regions can be operated substantially inparallel (e.g., for read, for erase, and/or for write).

In FIG. 12, the settings (197) are optimized to the usage pattern ofenhanced endurance in cyclic sequential overwrite in recording acontinuous stream of input data that is sampled at a fixed timeinterval.

FIG. 13 illustrates the configuration of a namespace (187) for theoutputs from artificial neurons according to one embodiment. Forexample, the configuration of FIG. 11 can be implemented in the datastorage device (101) illustrated in FIGS. 7 and/or 10. For example, thesettings (197) of FIG. 11 can be part of the namespace optimizationsettings (192) of FIG. 10.

The configuration of FIG. 13 maps a neuron output namespace (187) to atleast one storage media component C (162). Preferably, the at least onestorage media component C (162) can be used by the controller (151) inparallel with storage media components (e.g., 161 and 163) that hoststhe other namespaces (e.g., 183 and 185) of ANN data. For example, thestorage media component C (162) can be in an integrated circuit packagethat is separate from the integrated circuit packages for the othernamespaces (e.g., 183 and 185). Alternatively, the storage mediacomponents (161 to 163) are formed on separate integrated circuit diesembedded in a same integrated circuit package. Alternatively, thestorage media components (161 to 163) can be formed on separate regionsof an integrated circuit die, where the separate regions can be operatedsubstantially in parallel (e.g., for read, for erase, and/or for write).

In FIG. 13, the settings (197) are optimized to the usage pattern ofbuffered data for periodic overwrite with random access. For example,memory units are configured via the optimization settings (193 to 197)to update/overwrite in the neuron output namespace (187) at a frequencyhigher than in the ANN model namespace (183), but lower than in theneuron input namespace (185). For example, the

FIG. 14 shows a method of predictive maintenance supported by a modelpartition according to one embodiment. For example, the method of FIG.14 can be implemented in a vehicle (111) of FIG. 1 or 2 using a datastorage device (101) of FIG. 7 or 10 and/or a storage media component(160) of FIG. 8. For example, the method of FIG. 14 can be used incombination with the method of FIGS. 6 and/or 9.

At block 241, non-volatile memory of a data storage device (101) isconfigured into a plurality of partitions (e.g., 183, 185, 187, . . . ).For example, the non-volatile memory can have the same type of memoryunits for storing data (e.g., NAND flash memory units); and the sametype of memory units in the different partitions (e.g., 183 to 187) canbe configured differently to optimize their performances according tothe usage patterns of the data stored in the different partitions (e.g.,183 to 187).

At block 243, the data storage device (101) stores, for the partitions(e.g., 183, 185, 187, . . . ) respectively, different sets of memoryoperation settings (e.g., 193, 195, 197) for different types of datarelated to an artificial neural network (125), where the partitions(e.g., 183, 185, 187, . . . ) include a model partition (e.g., 193)configured to store model data of the artificial neural network (125).

At block 245, the data storage device (101) receives a sensor datastream (e.g., 121) from at least one sensor (103) configured on avehicle (111).

At block 247, a controller (151) of the data storage device (101)operates memory units in the partitions (183, 185, 187, . . . )according to the sets of memory operation settings (e.g., 193, 195, 197)in response to the sensor data stream (e.g., 121).

At block 249, the computer system (131) having the data storage device(101) predicts, using the artificial neural network (125), a maintenanceservice of the vehicle (111) based on the sensor data stream (e.g.,121).

For example, the memory operation settings configure the model partition(e.g., 183) to store three or more bits per memory cell. The memoryoperating settings can include address map (191) to map between neuronsin the ANN (125) and inputs to the neurons. When a first address of aninput to a neuron in the artificial neural network (125) is received,the first address in an input partition (e.g., 185) separate from themodel partition (e.g., 183) can be converted into at least one secondaddress of model data associated with the neuron, such that theattributes of the neuron and the identities of neurons connected to theneuron can be retrieved from the model partition (e.g., 183) without anexplicit command from the processors (133). The controller (151) canautomatically retrieve, from the model partition (e.g., 183), the modeldata associated with the neuron using the at least one second address,in response to the receiving of the first address. A neural networkaccelerator (159) can generate an output of the neuron from the input tothe neuron and the model data associated with the neuron. In general,input to the neuron can include outputs from multiple neurons that areconnected to the neuron in the ANN (125). The controller (151) can savethe output of the neuron in the buffer (152) in the data storage device(101) to facilitate accelerated access to the output by the hostprocessor(s) (133) and/or the neural network accelerator (159).

Typically, the model data does not change during computation to predictthe maintenance service. For example, the model data can include neuronconnectivity data of the artificial neural network and static attributesof neurons in the artificial neural network. The memory operationsettings (e.g., 192) can configure the model partition (e.g., 183) tostore more than one bit per memory cell in the non-volatile memory basedon the usage pattern of mostly read, infrequent update of the modeldata.

For example, the partitions (e.g., 183, 185, 187, . . . ) in the datastorage devices can be implemented as namespaces in which logicaladdresses are defined; and an address map (191) in the data storagedevice is configured to map the namespaces (183, 185, 187, . . . ) toseparate storage media components (e.g., 161, 163, 162, . . . ).

The model data in the model namespace (183) is updatable during trainingvia machine learning (171 or 175), or during over-the-air update of theANN (125) from the server (119).

In some implementations, the controller (151) is configured, via theaddress map (191) to retrieve, from the model partition, model dataassociated with a neuron in the artificial neural network, in responseto an input to, or an output from, the neuron being addressed in apartition separate from the model partition (183). Further, thecontroller (151) can retrieve, from the model partition (183), the modeldata associated with the neuron in parallel with storing input to theneuron in the partition (e.g., 185) that is separate from the modelpartition (183).

FIG. 15 shows a method of predictive maintenance supported by an inputpartition according to one embodiment. For example, the method of FIG.15 can be implemented in a vehicle (111) of FIG. 1 or 2 using a datastorage device (101) of FIG. 7 or 10 and/or a storage media component(160) of FIG. 8. For example, the method of FIG. 15 can be used incombination with the method of FIGS. 6, 9, and/or 14.

At block 261, non-volatile memory of a data storage device (101) isconfigured into a plurality of partitions (e.g., 183, 185, 187, . . . ).For example, the non-volatile memory can have the same type of memoryunits (e.g., NAND flash memory units) implemented in multiple storagemedia components (e.g., 161 to 163).

At block 263, the data storage device (101) stores, for the partitions(e.g., 183, 185, 187, . . . ) respectively, different sets of memoryoperation settings (e.g., 193, 195, 197) for different types of datarelated to an artificial neural network (125), where the partitions(e.g., 183, 185, 187, . . . ) include an input partition (e.g., 185)configured to cyclically store input data for the artificial neuralnetwork (125).

For example, the input partition (185) can be configured to storeexternal inputs for the artificial neural network (125) but not internalinputs. The input data stored in the input partition (185) isindependent of outputs from neurons in the artificial neural network(125).

For example, the input data stored in the input partition (185) caninclude a portion of the sensor data stream (e.g., 121). In someembodiments, the input data stored in the input partition (185) iscomputed from the sensor data stream (e.g., 121) for a subset of neuronsin the artificial neural network (125).

For example, the memory operation settings (e.g., 195) configure theinput partition (185) to store one bit per NAND memory cell in thenon-volatile memory for enhanced endurance for repeated data erasure anddata programming.

For example, the memory operation settings (e.g., 195) configure thecontroller to write sequentially the input data into to the inputpartition (185), and to overwrite oldest input data in the inputpartition (185) with the most recent input data received in the datastorage device (101).

At block 265, the data storage device (101) receives a sensor datastream (e.g., 121) from at least one sensor (103) configured on avehicle (111).

At block 267, a controller (151) of the data storage device (101)operates memory units in the partitions (183, 185, 187, . . . )according to the sets of memory operation settings (e.g., 193, 195, 197)in response to the sensor data stream (e.g., 121).

At block 269, the computer system (131) having the data storage device(101) predicts, using the artificial neural network (125), a maintenanceservice of the vehicle (111) based on the sensor data stream (e.g.,121).

FIG. 16 shows a method of predictive maintenance supported by an inputpartition according to one embodiment. For example, the method of FIG.16 can be implemented in a vehicle (111) of FIG. 1 or 2 using a datastorage device (101) of FIG. 7 or 10 and/or a storage media component(160) of FIG. 8. For example, the method of FIG. 16 can be used incombination with the method of FIGS. 6, 9, 14, and/or 15.

At block 281, non-volatile memory of a data storage device (101) isconfigured into a plurality of partitions (e.g., 183, 185, 187, . . . ).The non-volatile memory can have the same type of memory units (e.g.,NAND flash memory units) for storing data.

At block 283, the data storage device (101) stores, for the partitions(e.g., 183, 185, 187, . . . ) respectively, different sets of memoryoperation settings (e.g., 193, 195, 197) for different types of datarelated to an artificial neural network (125), where the partitions(e.g., 183, 185, 187, . . . ) include an output partition (e.g., 187)configured to store output data for the artificial neural network (125).

For example, the output data stored in the output partition (e.g., 187)can include state data of neurons in the artificial neural network(125). For example, the state data of the neurons in the artificialneural network can identify the activation levels of the neurons forspiking in a spiking neural network. The activation levels can becontrolled via a differential equation. Thus, the activation levels canchange in response to inputs to the artificial neural network (125)and/or in response to the passage of time.

For example, the output data can include the predictions orclassifications generated by the artificial neural network (125)responsive to the sensor data stream.

For example, the memory operation settings configure the outputpartition to store no more than two bits per memory cell in thenon-volatile memory.

At block 285, the data storage device (101) receives a sensor datastream (e.g., 121) from at least one sensor (103) configured on avehicle (111).

At block 287, a controller (151) of the data storage device (101)operates memory units in the partitions (183, 185, 187, . . . )according to the sets of memory operation settings (e.g., 193, 195, 197)in response to the sensor data stream (e.g., 121).

At block 289, the computer system (131) having the data storage device(101) predicts, using the artificial neural network (125), a maintenanceservice of the vehicle (111) based on the sensor data stream (e.g.,121).

For example, the data storage device (101) can include a buffer (152).The buffer (152) can be implemented via volatile memory (e.g., SRAM orDRAM) for access performance faster than the non-volatile memory (e.g.,NAND flash memory) of the data storage device (101). The memoryoperation settings configure the controller (151) to store the outputdata in the buffer (152) for access by a processor (e.g., 133) via thehost interface (157) during or after storing the output data into theoutput partition (187).

For example, the data storage device (101) can include a neural networkaccelerator (159) coupled to the controller (151). The neural networkaccelerator is configured to apply inputs provided to neurons in theartificial neural network (125) to model data of the artificial neuralnetwork (125) to generate the output data by one or more output neuronsin the artificial neural network (125). In response to the neuralnetwork accelerator (159) completing the computation of the output data,the controller is configured to provide the processor (e.g., 133) withan indication of the availability of the output data generated by theartificial neural network (125), such that the processor (e.g., 133) mayrequest the data storage device (101) to transmit the output data.

Optionally, the controller (151) is configured to provide the outputdata to the processor in parallel with storing the output data into theoutput partition. For example, the controller (151) can be configured toautomatically discard the output data computed for the previouslysegment of sensor data stream if the processor (e.g., 133) does notrequest for the transmission of the output data to the processor (e.g.,133) within a predetermined period of time, or before the next versionof the output data is available. Optionally, after reporting theavailability of the output data to the processor (e.g., 133), thecontroller (151) can be configured to selectively discard the outputdata computed for the previously segment of sensor data stream based ona response of the processor (e.g., 133) for the output data to theprocessor (e.g., 133). For example, the processor (e.g., 133) mayrequest the transmission of the output data to the processor (e.g., 133)without saving the output data into the output partition (e.g., 187) insome situations; and in other situations, the processor (e.g., 133) mayrequest the transmission of the output data to the processor (e.g., 133)and the storing of the output data into the output partition (e.g.,187).

Optionally, output data from the artificial neural network (125) canalso be stored into the output partition in a cyclic way (e.g., for thesegments of output data for time periods selected by the processor(e.g., 133)).

For example, external inputs to the artificial neural network (125) canbe recorded in the input namespace (185) continuously for the last timeperiod of T1. When the sensor data is sampled at a predetermined timeinterval T2, the input namespace (185) can hold the latest T1/T2 sets ofinput data. In contrast, external outputs from the artificial neuralnetwork (125) can be selectively recorded into the output namespace(187) (e.g., once for every a predetermined period of time T3, where T3is multiple of T2). The output data can be recorded into the outputnamespace (187) at a lower frequency; and the output namespace (187) canbe allocated to store a predetermined number of sets of output data(e.g., via sequential writes and writes in a cyclic way to keep the lastsets of output data).

At least some embodiments disclosed herein include a communicationprotocol/interface that allows a data storage device to perform neuralnetwork acceleration on the fly with reduced data traffic to the hostprocessor (e.g., a central processing unit (CPU)).

For example, the host processor (e.g., 133) of a vehicle (111) canprovide write commands to the data storage device (101) to store themodel of an artificial neural network in a model partition (e.g., 183).Since the neural network accelerator (159) is configured to apply themodel, the data communications sending back the data of the model of ANN(125) to the processor can be reduced or eliminated.

To use the ANN model in classifications and/or predictions, the hostprocessor (e.g., 133) of a vehicle (111) can stream input data for theANN (125) into the neuron input partition (e.g., 185). The neuralnetwork accelerator (159) of the storage device (101) can automaticallyapply the input data to the model stored in ANN model partition (e.g.,183) in accordance with the address map (191). The data storage device(101) makes the computed outputs available for propagation in the ANN(125). Preferably, the computed outputs are made available to the neuralnetwork accelerator (159) through the buffer (152) without the need tostore the intermediate outputs into storage media components (e.g., 161to 163). Thus, the data communications between the host processor (e.g.,133) and the data storage device (101) for the transporting of outputsof neurons can be reduced. When the outputs have propagated to theoutput neurons in the ANN (125), the data storage device (101) canprovide a response to the write request associating with the writing ofa set of input data into the neuron input partition (e.g., 185). Theresponse indicates that the external output from neurons in the ANN(125) is available. In response, the host processor (e.g., 133) of avehicle (111) can optionally issue read commands to retrieve theexternal outputs for further processing.

FIG. 17 shows communications with a data storage device (101) toimplement neural network computation according to one embodiment. Forexample, the communications as illustrated in FIG. 17 can be implementedin the vehicle (111) of FIG. 1 or 2, with a data storage device (101)illustrated in FIG. 7 or 10.

In FIG. 17, the processor(s) (133) can be configured with a simplifiedset of instructions (301) to perform neural network computation, sincesome of the computations involving the ANN (125) is performed by theneural network accelerator (159) within the data storage device (101).Thus, it is not necessary to transport the model data back to theprocessor(s) (133) during the use of the ANN (125) for predictionsand/or classifications.

The sensor(s) (103) can generate a continuous stream of sensor data(121) based on a rate for sampling data. The sensor data (121) can besampled at a fixed, predetermined time interval (e.g., during theoperation of the vehicle (111)). The processor(s) (133) can execute theinstructions (301) to convert the sensor data (121) into an input stream(303) for input neurons in the ANN (125). Input neurons in the ANN (125)are configured to accept external inputs to the ANN (125); and outputneurons are configured to provide external outputs from the ANN (125).

In general, a complete set of input for the ANN (125) at a time instanceincludes inputs for the entire set of input neurons of the ANN (125).The input stream (303) includes a sequence of input sets for a sequenceof time instances that are spaced apart from each other according to thefixed, predetermined time interval.

The data storage device (101) stores the input stream (303) into theneuron input namespace (185) in a cyclic way where the oldest input setcorresponding to the oldest time instance of data sampling for data setscurrently stored in the neuron input namespace (185) is erased to storethe newest set of inputs in the input stream (303).

For each input data set, the neural network accelerator (159) appliesthe model of the ANN (125) stored in the ANN model namespace (183). Theneural network accelerator (159) (or the processor(s) (133)) can controlthe propagation of signals within the neural network. When the outputneurons of the ANN (125) generate their outputs responsive to the inputdata set, the data storage device (101) can provide to the processor(133) an indication that the neuron output are ready for retrieval. Theindication can be configured in a response to the request from theprocessor(s) (133) to write the input data set into the neuron inputnamespace (185). The processor(s) (133) can optionally retrieve theoutput data (305) (e.g., in accordance with conditions and/or criteriaprogrammed in the instructions).

In some embodiments, a trigger parameter is configured in the datastorage device (101). When an output parameter in the external output(317) meetings a requirement specified by the trigger parameter, thedata storage device provides the response to the request from theprocessor(s) (133) to write the input data set into the neuron inputnamespace (185).

FIG. 18 shows communications within a data storage device to implementneural network computation according to one embodiment. For example, thecommunications of FIG. 18 can be implemented a data storage device (101)illustrated in FIG. 7 or 10, in connection with the communications ofFIG. 17.

In FIG. 18, the model namespace (183) stores the model (313) of theentire ANN (125). In response to receiving a set of external input (315)for a time instance from the input stream (303) in the buffer (152), thedata storage device (101) can write the external input (315) into theinput namespace (185) in parallel with retrieving a neuron model (312)containing a portion of the ANN model (313) corresponding to theparameters of the input neurons and/or the identities of neuronsconnected to the input neurons. The buffer (152) allows the neuralnetwork accelerator (159) to combine the neuron model (312) and theexternal input (325) to generate the output (327) of the input neurons.

In general, the neuron output (327) can include a portion that is theinternal output (316) for further propagation within the ANN (125)and/or a portion that is the external output (317) for the processor(s)(133).

The internal output (316) is stored in the buffer (152) as internalinput (316) for further propagation in the ANN (125) in a way similar tothe generation of neuron outputs (327) from the external input (315).For example, a portion of the internal input (316) can cause thecontroller (151) and/or the neural network accelerator (159) to retrievecorresponding neuron model (312) relevant to the internal input suchthat the internal input is applied in the neural network accelerator(159) to the corresponding neuron model (312) to generate their neuronoutputs (327).

When the complete set of external output (317) is available in thebuffer (152), the external output (317) can be stored in the outputnamespace (187).

Optionally, the storage device (101) does not store each set of externaloutput (317) corresponding to a set of stored external input (315)sampled at a time instance. For example, the storage device (101) can beconfigured to store one set of external output (317) for every apredetermined number of sets of external input (e.g., 315).Alternatively, or in combination, the processor(s) (133) can determinewhether or not to store the external output (317). For example, thestorage device (101) can be configured to store the external output(317) in response to the processor(s) (133) retrieving the externaloutput (317) for further processing. For example, the storage device(101) can be configured to store the external output (317) in responseto a write command from the processor(s) (133) after the processing ofthe external output (317) in the processor(s) (133).

FIG. 19 shows a method of communicating with a data storage device toimplement neural network computation according to one embodiment. Forexample, the method of FIG. 19 can be implemented in a vehicle (111) ofFIG. 1 or 2 using a data storage device (101) of FIG. 7 or 10 and/or astorage media component (160) of FIG. 8. For example, the method of FIG.19 can be used in combination with the method of FIGS. 6, 9, 14, 15,and/or 16.

At block 341, one or more processors (133) of a vehicle (111) storesmodel data (e.g., 313) of an artificial neural network (e.g., 125) intoa data storage device (101).

At block 343, the one or more processors (133) of the vehicle (111)receive, from at least one sensor (103) configured on vehicle (111), aset of sensor data.

At block 345, the one or more processors (133) of the vehicle (111)generate a set of inputs to the artificial neural network (e.g., 125)based on the sensor data.

At block 347, the one or more processors (133) of the vehicle (111)provide the set of inputs to the data storage device (101). In responseto the set of inputs, the data storage device (101) is configured togenerate a set of outputs using the model data (313) of the artificialneural network (e.g., 125).

At block 349, the one or more processors (133) of the vehicle (111)retrieve the set of outputs from the data storage device (101).

For example, the data storage device (101) generates the set of outputsusing at least a portion of the model data (183) stored in the datastorage device without transmitting the portion of the model data (183)to the one or more processors (133) between the receiving of the set ofinputs and the completion of the computation of the set of outputs.

For example, the portion of the model data (183) can include staticattributes of neurons in the artificial neural network (e.g., 125)and/or the neuron connectivity data of the artificial neural network(e.g., 125).

For example, to provide of the set of inputs to the data storage device(101), the one or more processors (133) of the vehicle (111) cantransmit one or more write commands to the data storage device (101).The one or more write commands are configured to instruct the datastorage device (101) to store the set of input in the data storagedevice (101). After the completion of the computation of the set ofoutputs in the data storage device (101), the controller (151) of thedata storage device (101) can transmit a response to the one or morewrite commands to the one or more processors (133). The response caninclude an indication that the set of outputs is available for retrievalby the one or more processors (133).

In response to the indication, the one or more processors (133) canoptionally retrieves of the set of outputs from the data storage device(101) by transmitting a read command to the data storage device (101)for the set of outputs (e.g., after a determination to retrieve the setof outputs from the data storage device (101) for processing).

Alternatively, or in combination, the one or more processors (133) ofthe vehicle (111) can determine whether to store the set of outputs innon-volatile memory of the data storage device. In response to adetermination to store the set of outputs in the non-volatile memory ofthe data storage device (101), one or more processors (133) of thevehicle (111) can transmit a write command to the data storage device(101).

Since the set of outputs is initially generated in the data storagedevice (101) and then buffered in the buffer (152) (e.g., volatilememory), the data storage device (101) can execute a write command tostore the set of outputs into an output namespace (187) withouttransmitting the set of outputs to the one or more processors (133)and/or receiving the set of outputs from the one or more processors(133) in response to the write commands.

For example, after receiving, from at least one sensor (103) configuredon the vehicle (111), a further set of sensor data (121), the one ormore processors (133) of the vehicle (111) generate a further set ofinputs to the artificial neural network (125) based on the further setof sensor data.

The one or more processors (133) transmits a further command to writethe further set of inputs into the data storage device (101); and thedata storage device (101) generates a further set of outputs using themodel data (183) of the artificial neural network (125) and the furtherset of inputs. After receiving a response to the further command towrite the further set of inputs, the one or more processors (133) candetermine to skip the processing of the further set of outputs andtransmit, to the data storage device (101), a subsequent write commandto store the further set of outputs. In response, the data storagedevice (101) can write the further set of outputs that is bufferedwithin the data storage device (101) into the output namespace (187),without the transmitting of the further set of outputs from the one ormore processors (133) of the vehicle (111) to the data storage device(101) and/or without the transmitting of the further set of outputs fromthe data storage device (101) to the one or more processors (133) of thevehicle (111).

At least some of the neural network techniques discussed above can beused in intelligent optimization of caching operations in a data storagedevice.

In general, different caching strategies/schemes and different operatingparameters for the strategies/schemes can have different performancelevels for different patterns of data access.

An artificial neural network (ANN) (e.g., spiking neural network,convolutional neural network, recurrent neural network) can beconfigured to make predictions for the determination of the idealcaching scheme in a data storage device (e.g., a solid state drive(SSD)) for the current data access pattern.

For example, a data storage device configured on an autonomous vehiclecan use an artificial neural network (ANN) to predict an optimizedcaching scheme based on data access patterns associated with variousfactors, such as the operating status of the vehicle, the processingstatus/milestone/workload of the Advanced Driver Assistance System(ADAS) of the vehicle, the activities of applications that have dataaccess requests, types of the access requests (e.g., read/write),frequency of the access requests, address locations of the accessrequests, chunk sizes of data involved in the access requests, etc. Theartificial neural network (ANN) can used to predict the optimizedcaching algorithm among a plurality of candidate caching algorithms andthe optimized parameters for caching operations performed suing theoptimized caching algorithm. Examples of the parameters used in cachingoperations performed using a caching strategy/scheme include cache size,cache block size, cache set size, number of cache sets, etc.

The artificial neural network (ANN) can be self-trained within the datastorage device. For example, an unsupervised machine learning techniquecan be used to train the artificial neural network (ANN) toself-organize input data to gain prediction/classification capability.

Alternatively, or in combination, a supervised machine learningtechnique can be used to train the artificial neural network (ANN) torefine or establish a prediction model of optimized caching scheme andits parameters.

For example, during a training period, the data storage device can vary(e.g., randomly) the use of caching schemes and their parameters totraining data that associates data access patterns, the caching schemesand their parameters, and the caching performance levels achieved usingthe respective caching schemes and parameters.

For example, data access patterns of a data storage device configured ina vehicle can be associated with operating parameters, such as thecurrent operating parameters of the vehicle, the current operatingparameters of applications (e.g., components or electronic control unitsof the vehicle) that are actively using the data storage device, and/orthe current operating parameters of the data storage device. Theoperating parameters can be provided as input to the artificial neuralnetwork (ANN) to indicate the data usage patterns.

When a caching scheme and its parameters are used to control the cachingoperation in the data storage device, the caching performance level canbe measured. For example, caching performance level indicators caninclude a ratio between cache hit and cache miss within a period time, acache hit rate, a cache miss rate, etc.

An unsupervised machine learning technique can be used to train theartificial neural network (ANN), using the data collected during thetraining period, to associate (e.g., via clustering, classification,etc.) data usage patterns, the caching scheme and parameters, and thecaching performance levels, which allows the determination of apreferred caching scheme and its parameters for the current data accesspattern that can optimize the caching performance level.

Optionally, or in combination, optimized caching schemes and theirparameters determined for various data access patterns can be used totrain the artificial neural network (ANN), using a supervised machinelearning technique, to minimize a difference between the predictedcaching schemes and their parameters and the optimized caching schemesand their parameters determined for the various data access patterns.

Thus, when the data storage device is operating under a data accesspattern represented by the current operating parameters, the artificialneural network (ANN) can be used to determine a cache scheme and itsparameters to optimize the caching performance in the data storagedevice.

Optionally, when the artificial neural network (ANN) classifies thecurrent data access pattern as a new pattern, the data storage devicecan try varying caching schemes and caching parameters to generatetraining data for the new pattern to determine an optimal cachingschemes and parameters for the new pattern.

FIG. 20 shows a data storage device configured to optimize cacheoperations according to one embodiment. For example, the data storagedevice of FIG. 20 can be implemented using the techniques of a datastorage device (101) of FIG. 7 or 10 in a vehicle (111) illustrated inFIG. 1 or 2.

In FIG. 20, the data storage device (101) includes a cache memory (351)that is configurable via cache configuration parameters (355) to adjustits caching operations. For example, the cache memory (351) can be usedto cache data that is stored in the storage media components (161 to163) and that may be subsequently used by the neural network accelerator(159) and/or the processor(s) (133) connected to the data storage device(101). For example, the cache memory (351) can be used to provide thefunctionality of the buffer (152) illustrated in FIGS. 10, 13, 17,and/or 18.

When controlled by a set of cache configuration parameters (355), thecaching operations of the cache memory (351) can have different cachinglevels measured by cache performance indicators (357), such as cache hitrate, cache miss rate, etc. For example, the cache configurationparameters (355) can include an identification of a caching strategyselected from a plurality of caching strategy candidates that can beimplemented for the cache memory (351). For example, the cacheconfiguration parameters (355) can include cache size, cache block size,cache set size, number of cache sets, etc.

The controller (151) of the data storage device (101) can provideoperating parameters (353) that are indicative of a data access patternin the data storage device (101).

Examples of the operating parameters (353) can include the operatingparameter of the vehicle (111) in which the data storage device (101) isinstalled. For example, the operating parameter of the vehicle (111) caninclude a speed of the vehicle (111), a location of the vehicle (111), aroadway on which the vehicle (111) is traveling, inputs from some of thesensors (103) of the vehicle (111), the status of vehicle controls(e.g., 141, 143, 145), the status of the components of the vehicle(111), such as the infotainment system (149) and/or the communicationdevice (139) of the vehicle (111). The operating parameter of thevehicle (111) can include the status and operations of the AdvancedDriver Assistance System (ADAS) (105) and/or otherapplications/components running in the vehicle (111).

Examples of the operating parameters (353) can include the operatingparameter of the data storage device (101), such as the status andoperation of the neural network accelerator (159), commands queued inthe data storage device (101) for execution, status of backgroundoperations to be performed and/or being performed in the data storagedevice (101), etc.

During a training period, the controller (151) generates training databy trying different cache configurations and measure their performancesunder various conditions. The training data can include the operatingparameters (353) indicative of the data access patterns, different cacheconfiguration parameters (355) used to control the operation of thecache memory (351), and cache performance indicators (357) of the cachememory (351) controlled by the respective cache configuration parameters(355) for the respective data access patterns indicated by the operatingparameters (353).

The neural network accelerator (159) can train the artificial neuralnetwork (125) using the training data to recognize data access patternsand/or predict cache performance indicators (357) for various cachingconfiguration parameters (355).

Based on the predictions by the neural network accelerator (159), theneural network accelerator (159) and/or the controller (151) candetermine an optimal set of cache configuration parameters (355) tocontrol the operations of the cache memory (351).

Optionally, or alternatively, when the data storage device (111) isoperating in a data access pattern that is recognized by the artificialneural network (125) based on the operating parameters (353), thecontroller (151) can vary the cache configuration parameters (355) tosearch for a cache configuration parameter (355) that optimizes thecache performance indicators (357).

The optimized set of cache configuration parameters (355) can bedetermined via a search performed by the controller (151) throughvarying cache configuration parameters (355) and/or via predictions ofcache performance indicators (357), predicted by the neural networkaccelerator (159) based on selected cache configuration parameters(355). The data of the optimized set of cache configuration parameters(355) can be used to further train the neural network accelerator (159)to predict the optimized set of cache configuration parameters for thecorresponding data access pattern.

The techniques of FIG. 20 can also be used to optimize cache operationsoutside of the data storage device (101). For example, cache operationsin the computer system (131) can be similarly optimized via anartificial neural network (125) in the data storage device (101), wherethe operating parameters (353) indicative of patterns of cache accesscan be provided to the neuron input namespace (185) in the data storagedevice (101). Predicted cache performance indicators (357), predictedconfiguration parameters (355), and/or predicted optimized configurationparameters (355) can be retrieved from the neuron output namespace (187)in the data storage device (101).

FIG. 21 shows a method of optimizing cache operations in a data storagedevice according to one embodiment. For example, the method of FIG. 21can be implemented in a vehicle (111) of FIG. 1 or 2 using a datastorage device (101) of FIGS. 7, 10, 20 and/or a storage media component(160) of FIG. 8. For example, the method of FIG. 21 can be used incombination with the method of FIGS. 6, 9, 14, 15, 16, and/or 19.

At block 361, a data storage device (101) stores data into, andretrieves data from, one or more storage media components (161 to 163)of the data storage device (101).

At block 363, a controller (151) of the data storage device (101)identifies operating parameters (353) indicative data access patterns inthe data storage device.

For example, the data storage device (101) can be configured in avehicle (111) to support the operations of the advanced driverassistance system (ADAS) (105) and/or the infotainment system (149). Theoperating parameters (353) can include operating parameters of thevehicle (111).

For example, the operating parameters (353) of the vehicle (111) caninclude a speed of the vehicle (111), a location of the vehicle (111),an input from a sensor (103) configured on the vehicle, a status of avehicle control (e.g., 141, 143, or 145), a status of an infotainmentsystem (149) of the vehicle (111), or a status of an advanced driverassistance system (ADAS) (105) of the vehicle (111), or any combinationthereof.

The operating parameters (353) provided as input to the artificialneural network (125) can include operating parameters of the dataprocessing system (101), such as a status of operations of the neuralnetwork accelerator (159), a status of pending commands to be executedby the controller (151), or a status of operations in progress in thestorage media components (161 to 163), or any combination thereof.

At block 365, the operating parameters (353) are provided as a functionof time to an artificial neural network (125) as input.

For example, the artificial neural network (125) can include a spikingneural network that is trained, in the data storage device (101), usinga training dataset generated in the data storage device.

At block 367, the artificial neural network (125) generates a predictionbased on the input to determine an optimized set of cache configurationparameters (355).

For example, the optimized set of cache configuration parameters (355)can include an identification of a caching strategy selected from aplurality of caching strategies usable for the cache memory, or a sizefor caching, or a combination thereof.

For example, the prediction can include an identification of a dataaccess pattern, a cache performance indicator of a set of cacheconfiguration parameters controlling caching operations of the cachememory for a data access pattern identified via the operatingparameters, or the optimized set of cache configuration parameters, orany combination thereof.

For example, the data storage device (101) can include a neural networkaccelerator (159) configured to generate the prediction using model data(313) of the artificial neural network (125) stored in the data storagedevice (101).

Optionally, the neural network accelerator (159) is further configuredto train the artificial neural network (125) using training datagenerated in the data storage device (101) in a training period. Forexample, the training data can include the operating parameters (353)identified in the training period, sets of cache configurationparameters (355) used in the training period, and cache performanceindicators (357) measured during the training period for the sets ofcache configuration parameters (355).

Optionally, the neural network accelerator (159) is further configuredto train the artificial neural network (125) using training datagenerated for a data access pattern. For example, the training data caninclude the operating parameters (353) that are classified by theartificial neural network (125) to have the data access pattern in atime period; and the training data can further include an optimized setof cache configuration parameters (355) determined for the data accesspattern recognized from the operating parameters (353) in the timeperiod.

Optionally, the neural network accelerator (159) is configured todetermine, for the training data, the optimized sets of cacheconfiguration parameters (355) based on a search facilitated bypredictions of cache performance indicators (357) generated by theartificial neural network (125) for varying cache configurationparameters (355).

Optionally, or in combination, the controller (151) is configured todetermine, for the training data, the optimized sets of cacheconfiguration parameters (355) based on a search facilitated bymeasuring performance levels (357) for varying cache configurationparameters (355) applied to the cache memory (351).

At block 369, the data storage device (101) configures cache operationsof a cache memory (351) of the data storage device, according to theoptimized set of cache configuration parameters (355) determined basedon the prediction generated (367) by the artificial neural network(125).

The server (119), the computer system (131), and/or the data storagedevice (101) can each be implemented as one or more data processingsystems.

The present disclosure includes methods and apparatuses which performthe methods described above, including data processing systems whichperform these methods, and computer readable media containinginstructions which when executed on data processing systems cause thesystems to perform these methods.

A typical data processing system may include includes an inter-connect(e.g., bus and system core logic), which interconnects amicroprocessor(s) and memory. The microprocessor is typically coupled tocache memory.

The inter-connect interconnects the microprocessor(s) and the memorytogether and also interconnects them to input/output (I/O) device(s) viaI/O controller(s). I/O devices may include a display device and/orperipheral devices, such as mice, keyboards, modems, network interfaces,printers, scanners, video cameras and other devices known in the art. Inone embodiment, when the data processing system is a server system, someof the I/O devices, such as printers, scanners, mice, and/or keyboards,are optional.

The inter-connect can include one or more buses connected to one anotherthrough various bridges, controllers and/or adapters. In one embodimentthe I/O controllers include a USB (Universal Serial Bus) adapter forcontrolling USB peripherals, and/or an IEEE-1394 bus adapter forcontrolling IEEE-1394 peripherals.

The memory may include one or more of: ROM (Read Only Memory), volatileRAM (Random Access Memory), and non-volatile memory, such as hard drive,flash memory, etc.

Volatile RAM is typically implemented as dynamic RAM (DRAM) whichrequires power continually in order to refresh or maintain the data inthe memory. Non-volatile memory is typically a magnetic hard drive, amagnetic optical drive, an optical drive (e.g., a DVD RAM), or othertype of memory system which maintains data even after power is removedfrom the system. The non-volatile memory may also be a random accessmemory.

The non-volatile memory can be a local device coupled directly to therest of the components in the data processing system. A non-volatilememory that is remote from the system, such as a network storage devicecoupled to the data processing system through a network interface suchas a modem or Ethernet interface, can also be used.

In the present disclosure, some functions and operations are describedas being performed by or caused by software code to simplifydescription. However, such expressions are also used to specify that thefunctions result from execution of the code/instructions by a processor,such as a microprocessor.

Alternatively, or in combination, the functions and operations asdescribed here can be implemented using special purpose circuitry, withor without software instructions, such as using Application-SpecificIntegrated Circuit (ASIC) or Field-Programmable Gate Array (FPGA).Embodiments can be implemented using hardwired circuitry withoutsoftware instructions, or in combination with software instructions.Thus, the techniques are limited neither to any specific combination ofhardware circuitry and software, nor to any particular source for theinstructions executed by the data processing system.

While one embodiment can be implemented in fully functioning computersand computer systems, various embodiments are capable of beingdistributed as a computing product in a variety of forms and are capableof being applied regardless of the particular type of machine orcomputer-readable media used to actually effect the distribution.

At least some aspects disclosed can be embodied, at least in part, insoftware. That is, the techniques may be carried out in a computersystem or other data processing system in response to its processor,such as a microprocessor, executing sequences of instructions containedin a memory, such as ROM, volatile RAM, non-volatile memory, cache or aremote storage device.

Routines executed to implement the embodiments may be implemented aspart of an operating system or a specific application, component,program, object, module or sequence of instructions referred to as“computer programs.” The computer programs typically include one or moreinstructions set at various times in various memory and storage devicesin a computer, and that, when read and executed by one or moreprocessors in a computer, cause the computer to perform operationsnecessary to execute elements involving the various aspects.

A machine readable medium can be used to store software and data whichwhen executed by a data processing system causes the system to performvarious methods. The executable software and data may be stored invarious places including for example ROM, volatile RAM, non-volatilememory and/or cache. Portions of this software and/or data may be storedin any one of these storage devices. Further, the data and instructionscan be obtained from centralized servers or peer to peer networks.Different portions of the data and instructions can be obtained fromdifferent centralized servers and/or peer to peer networks at differenttimes and in different communication sessions or in a same communicationsession. The data and instructions can be obtained in entirety prior tothe execution of the applications. Alternatively, portions of the dataand instructions can be obtained dynamically, just in time, when neededfor execution. Thus, it is not required that the data and instructionsbe on a machine readable medium in entirety at a particular instance oftime.

Examples of computer-readable media include but are not limited tonon-transitory, recordable and non-recordable type media such asvolatile and non-volatile memory devices, read only memory (ROM), randomaccess memory (RAM), flash memory devices, floppy and other removabledisks, magnetic disk storage media, optical storage media (e.g., CompactDisk Read-Only Memory (CD ROM), Digital Versatile Disks (DVDs), etc.),among others. The computer-readable media may store the instructions.

The instructions may also be embodied in digital and analogcommunication links for electrical, optical, acoustical or other formsof propagated signals, such as carrier waves, infrared signals, digitalsignals, etc. However, propagated signals, such as carrier waves,infrared signals, digital signals, etc. are not tangible machinereadable medium and are not configured to store instructions.

In general, a machine readable medium includes any mechanism thatprovides (i.e., stores and/or transmits) information in a formaccessible by a machine (e.g., a computer, network device, personaldigital assistant, manufacturing tool, any device with a set of one ormore processors, etc.).

In various embodiments, hardwired circuitry may be used in combinationwith software instructions to implement the techniques. Thus, thetechniques are neither limited to any specific combination of hardwarecircuitry and software nor to any particular source for the instructionsexecuted by the data processing system.

The above description and drawings are illustrative and are not to beconstrued as limiting. Numerous specific details are described toprovide a thorough understanding. However, in certain instances, wellknown or conventional details are not described in order to avoidobscuring the description. References to one or an embodiment in thepresent disclosure are not necessarily references to the sameembodiment; and, such references mean at least one.

In the foregoing specification, the disclosure has been described withreference to specific exemplary embodiments thereof. It will be evidentthat various modifications may be made thereto without departing fromthe broader spirit and scope as set forth in the following claims. Thespecification and drawings are, accordingly, to be regarded in anillustrative sense rather than a restrictive sense.

What is claimed is:
 1. A data storage device, comprising: one or morestorage media components; a controller configured to store data into andretrieve data from the one or more storage media components; a cachememory configured to cache data that is stored in the one or morestorage media components; and an artificial neural network configured toreceive, as input and as a function of time, operating parametersindicative a data access pattern, and generate, based on the input, aprediction to determine an optimized set of cache configurationparameters; wherein cache operations of the cache memory are configuredaccording to the optimized set of cache configuration parametersdetermined based on the prediction.
 2. The data storage device of claim1, wherein the artificial neural network includes a spiking neuralnetwork.
 3. The data storage device of claim 2, wherein the data storagedevice is configured in a vehicle; and the operating parameters includeoperating parameters of the vehicle.
 4. The data storage device of claim3, wherein the operating parameters of the vehicle include a speed ofthe vehicle, a location of the vehicle, an input from a sensorconfigured on the vehicle, a status of a vehicle control, a status of aninfotainment system of the vehicle, or a status of an advanced driverassistance system of the vehicle, or any combination thereof.
 5. Thedata storage device of claim 2, wherein the optimized set of cacheconfiguration parameters includes an identification of a cachingstrategy selected from a plurality of caching strategies, or a size forcaching, or a combination thereof.
 6. The data storage device of claim2, wherein the prediction includes an identification of a data accesspattern.
 7. The data storage device of claim 2, wherein the predictionincludes a cache performance indicator of a set of cache configurationparameters controlling caching operations of the cache memory for a dataaccess pattern identified via the operating parameters.
 8. The datastorage device of claim 2, wherein the prediction includes the optimizedset of cache configuration parameters.
 9. The data storage device ofclaim 2, further comprising: a neural network accelerator configured togenerate the prediction using model data of the artificial neuralnetwork stored in the data storage device.
 10. The data storage deviceof claim 9, wherein the neural network accelerator is further configuredto train the artificial neural network using training data generated ina training period; and wherein the training data includes the operatingparameters in the training period, sets of cache configurationparameters used in the training period, and caching performanceindicators measured during the training period for the sets of cacheconfiguration parameters.
 11. The data storage device of claim 9,wherein the neural network accelerator is further configured to trainthe artificial neural network using training data; and wherein thetraining data includes the operating parameters in a time period, andoptimized sets of cache configuration parameters determined for dataaccess patterns recognized from the operating parameters in the timeperiod.
 12. The data storage device of claim 11, wherein the neuralnetwork accelerator is configured to determine the optimized sets ofcache configuration parameters based on predictions of cachingperformance indicators for varying cache configuration parameters. 13.The data storage device of claim 11, wherein the controller isconfigured to determine the optimized sets of cache configurationparameters based on measuring performance levels for varying cacheconfiguration parameters applied to the cache memory.
 14. A method,comprising: storing data into and retrieving data from one or morestorage media components of a data storage device; identifying, by acontroller of the data storage device, operating parameters indicativedata access patterns in the data storage device; providing, as input,the operating parameters as a function of time to an artificial neuralnetwork; generating, using the artificial neural network, a predictionbased on the input to determine an optimized set of cache configurationparameters; and configuring cache operations of a cache memory of thedata storage device, according to the optimized set of cacheconfiguration parameters determined based on the prediction.
 15. Themethod of claim 14, wherein the artificial neural network includes aspiking neural network; and the method further comprises: training, inthe data storage device, the artificial neural network using a trainingdataset generated in the data storage device.
 16. The method of claim15, wherein the training dataset includes the operating parameterscollected in a training period, sets of cache configuration parametersused in the training period, and caching performance indicators measuredduring the training period for the sets of cache configurationparameters.
 17. The method of claim 15, wherein the training datasetincludes an identification of a data access pattern associated with theoperating parameters collected in a time period, and an optimized set ofcache configuration parameters determined for the data access pattern.18. The method of claim 17, further comprising: searching for theoptimized set of cache configuration parameter by varying cacheconfiguration parameters provided as input to the artificial neuralnetwork to predict performance level indicators, or by varying cacheconfiguration parameters used for the data access pattern to measurerespective performance level indicators.
 19. A vehicle, comprising: acomputer system configured to generate operating parameters of thevehicle as a function of time; and a data storage device configured toidentify operating parameters of the data storage device, wherein theoperating parameters of the vehicle and the operating parameters of thedata storage device are indicative of data access patterns in the datastorage device; wherein the data storage device includes a cache memorycontrollable by different sets of cache configuration parameters;wherein the data storage device is configured to generate, based on theoperating parameters of the vehicle and the operating parameters of thedata storage device as input to an artificial neural network, aprediction to determine an optimized set of cache configurationparameters; and wherein cache operations of the cache memory areconfigured according to the optimized set of cache configurationparameters determined based on the prediction.
 20. The vehicle of claim19, wherein the artificial neural network includes a spiking neuralnetwork; and the data storage device is further configured to generatetraining data and train the spiking neural network to generate theprediction.